AI-Driven Virtual Health Systems Integrating Federated Learning for Decentralized Telemedicine

Telemedicine is using telecommunications and digital technologies to deliver healthcare services remotely. Integrating Artificial Intelligence (AI) in telemedicine has revolutionized virtual health systems by enabling personalized and efficient healthcare delivery. However, centralising medical data raises significant challenges, including privacy concerns, security risks, and regulatory constraints. Federated Learning (FL) provides a decentralized framework to overcome these challenges by providing collaborative model training across distributed datasets without compromising patient confidentiality. The paper proposes a method, FL-AIVHS, to design an AIdriven virtual health system (VHS) integrating Federated Learning to enhance data security, improve predictive accuracy, and enable equitable access to telemedicine solutions. The FL-AIVHS leverages FL for distributed model training on anonymised patient data from multiple healthcare providers. Advanced deep-learning models like convolutional neural networks (CNN) are utilized for disease prediction, while privacy-preserving techniques are used to ensure data security. Key findings demonstrate a 35% improvement in model accuracy compared to traditional centralized training methods and a significant reduction in privacy breach risks. The system also performs well across different datasets and complies with data protection standards like General Data Protection Regulation (GDPR). In conclusion, the AI-driven virtual health system integrating Federated Learning provides a transformative approach to decentralized telemedicine, addressing privacy, scalability, and accessibility challenges in modern healthcare.